Simone Frintrop
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Abstract
Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discussion on the limitations and open questions in the field.
- Abdi, H. 2007. Signal detection theory (SDT). In Encyclopedia of Measurement and Statistics, N. Salkind, Ed. Sage, CA.Google Scholar
- Aloimonos, Y., Weiss, I., and Bandopadhay, A. 1988. Active vision. Int. J. Comput. Vision 1, 4, 333--356.Google ScholarCross Ref
- Aristotle. On Sense and the Sensible. The Internet Classics Archive, 350 B.C.E., translated by J. I. Beare.Google Scholar
- Awh, E. and Pashler, H. 2000. Evidence for split attentional foci. J. Exp.Psych. Hum. Percept. Perform. 26, 2, 834--846.Google ScholarCross Ref
- Aziz, M. Z. and Mertsching, B. 2007. Pop-out and IOR in static scenes with region-based visual attention. In Proceedings of the ICVS Workshop on Computational Attention and Applications. ACM, New York.Google Scholar
- Backer, G. 2004. Modellierung visueller Aufmerksamkeit im Computer-Sehen: Ein zweistufiges Selektionsmodell für ein Aktives Sehsystem. Ph.D. thesis, Universität Hamburg, Germany.Google Scholar
- Backer, G., Mertsching, B., and Bollmann, M. 2001. Data- and model-driven gaze control for an active-vision system. IEEE Trans. Pattern Anal. Mach. Intell. 23, 12, 1415--1429. Google ScholarDigital Library
- Bacon, W. and Egeth, H. 1994. Overriding stimulus-driven attentional capture. Percept. Psychophysics 55, 5, 485--496.Google ScholarCross Ref
- Baddeley, R. J. and Tatler, B. W. 2006. High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis. Vision Res. 46, 2824--2833.Google ScholarCross Ref
- Balkenius, C. 2000. Attention, habituation and conditioning: towards a computational model. Cognitive Sci. Q. 1, 2, 171--214.Google Scholar
- Baluja, S. and Pomerleau, D. 1997. Expectation-based selective attention for visual monitoring and control of a robot vehicle. Rob. Auton. Syst. 22, 3-4, 329--344.Google ScholarCross Ref
- Belardinelli, A. 2008. Salience features selection: Deriving a model from human evidence. Ph.D. thesis, Sapienza Universita di Roma, Rome, Italy.Google Scholar
- Ben-Shahar, O., Scholl, B., and Zucker, S. 2007. Attention, segregation, and textons: Bridging the gap between object-based attention and texton-based segregation. Vision Res. 47, 6, 173--178.Google ScholarCross Ref
- Bichot, N. P. 2001. Attention, eye movements, and neurons: Linking physiology and behavior. In Vision and Attention, M. Jenkin and L. R. Harris, Eds. Springer Verlag, Berlin.Google Scholar
- Bichot, N. P., Rossi, A. F., and Desimone, R. 2005. Parallel and serial neural mechanisms for visual search in macaque area V4. Science 308, 5721, 529--534.Google Scholar
- Bisley, J. and Goldberg, M. 2003. Neuronal activity in the lateral intraparietal area and spatial attention. Science 299, 5603, 81--86.Google Scholar
- Björkman, M. and Eklundh, J.-O. 2007. Vision in the real world: Finding, attending and recognizing objects. Int. J. Imaging Syst. Technol. 16, 2, 189--208.Google ScholarCross Ref
- Bollmann, M. 1999. Entwicklung einer Aufmerksamkeitssteuerung für ein aktives Sehsystem. Ph.D. thesis, Universität Hamburg, Germany.Google Scholar
- Bollmann, M., Hoischen, R., Jesikiewicz, M., Justkowski, C., and Mertsching, B. 1999. Playing domino: A case study for an active vision system. In Computer Vision Systems, H. Christensen, Ed. Springer, Berlin, 392--411. Google ScholarDigital Library
- Borji, A. 2009. Interactive learning of task-driven visual attention control. Ph.D. thesis, Institute for Research in Fundamental Sciences (IPM), School of Cognitive Sciences (SCS), Tehran, Iran.Google Scholar
- Breazeal, C. 1999. A context-dependent attention system for a social robot. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 99). Springer, Berlin, 1146--1151. Google ScholarDigital Library
- Bruce, N. D. B. and Tsotsos, J. K. 2005a. An attentional framework for stereo vision. In Proceedings of the Canadian Conference on Computer and Robot Vision. IEEE, Los Alamitos. Google ScholarDigital Library
- Bruce, N. D. B. and Tsotsos, J. K. 2005b. Saliency-based on information maximization. In Proceedings of Neural Information Processing Systems (NIPS). MIT Press, Cambridge, MA.Google Scholar
- Bundesen, C. 1990. A theory of visual attention. Psych. Rev. 97, 523--547.Google ScholarCross Ref
- Bundesen, C. 1998. A computational theory of visual attention. Philos. Trans. R. Soc., Series B 353, 1271--1281.Google ScholarCross Ref
- Bundesen, C. and Habekost, T. 2005. Attention. In Handbook of Cognition, K. Lamberts and R. Goldstone, Eds. Sage Publications, London.Google Scholar
- Bur, A., Wurtz, P., Müri, R., and Hügli, H. 2007. Motion integration in visual attention models for predicting simple dynamic scenes. In Proceedings of the SPIE Conference on Human Vision and Electronic Imaging. Springer, Berlin.Google Scholar
- Cameron, E., Tai, J., Eckstein, M., and Carrasco, M. 2004. Signal detection theory applied to three visual search tasks. Spatial Vision 17, 4--5.Google Scholar
- Carrasco, M., Evert, D. L., Chang, I., and Katz, S. M. 1995. The eccentricity effect: Target eccentricity affects performance on conjunction searches. Percept. Psychophysics 57, 8, 1241--1261.Google ScholarCross Ref
- Cassin, B. and Solomon, S. 1990. Dictionary of Eye Terminology. Triad Publishing Company, Gainsville, FL.Google Scholar
- Cave, K. R. 1999. The Feature Gate model of visual selection. Psych. Res. 62, 182--194.Google ScholarCross Ref
- Cave, K. R. and Wolfe, J. M. 1990. Modeling the role of parallel processing in visual search. Cognitive Psych. 22, 2, 225--271.Google ScholarCross Ref
- Cherry, E. C. 1953. Some experiments on the recognition of speech, with one and two ears. J. Acoust. Soc. Am. 25, 975--979.Google ScholarCross Ref
- Choi, S.-B., Ban, S.-W., and Lee, M. 2004. Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition. Neural Inform. Process. Lett. Rev. 2, 1.Google Scholar
- Chun, M. M. and Jiang, Y. 1998. Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psych. 36, 28--71.Google ScholarCross Ref
- Clark, J. J. and Ferrier, N. J. 1988. Modal control of an attentive vision system. In Proceedings of the 2nd International Conference on Computer Vision. IEEE, Los Alamitos, CA.Google Scholar
- Clark, J. J. and Ferrier, N. J. 1989. Control of visual attention in mobile robots. In Proceedings of the IEEE Conference on Robotics and Automation. IEEE, Los Alamitos, CA, 826--831.Google Scholar
- Clark, J. J. and Ferrier, N. J. 1992. Attentive visual serving. In An Introduction to Active Vision, A. Blake and A. Yuille, Eds. MIT Press, Cambridge, MA. Google ScholarDigital Library
- Connor, C. E., Egeth, H. E., and Yantis, S. 2004. Visual attention: Bottom-up versus top-down. Curr. Biol. 14.Google Scholar
- Corbetta, M. 1990. Frontoparietal cortical networks for directing attention and the eye to visual locations: Identical, independent, or overlapping neural systems? In Proceedings of the National Academy of Sciences of the United States of America 95, 831--838.Google Scholar
- Corbetta, M. and Shulman, G. L. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. 3, 3, 201--215.Google ScholarCross Ref
- Dankers, A., Barnes, N., and Zelinsky, A. 2007. A reactive vision system: Active-dynamic saliency. In Proceedings of the 5th International Conference on Computer Vision Systems (ICVS'07). IEEE, Los Alamitos, CA.Google Scholar
- Desimone, R. and Duncan, J. 1995. Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193--222.Google ScholarCross Ref
- Deubel, H. and Schneider, W. X. 1996. Saccade target selection and object recognition: Evidence for a common attentional mechanism. Vision Res. 36, 12, 1827--1837.Google ScholarCross Ref
- Draper, B. A. and Lionelle, A. 2005. Evaluation of selective attention under similarity transformations. J. Comput. Vision Image Understanding 100, 1-2, 152--171. Google ScholarDigital Library
- Driver, J. and Baylis, G. C. 1998. Attention and visual object segmentation. In The Attentive Brain, R. Parasuraman, Ed. MIT Press, Cambridge, MA, 299--326.Google Scholar
- Duncan, J. 1984. Selective attention and the organization of visual information. J. Exp. Psych. 113, 501--517.Google ScholarCross Ref
- Eckstein, M., Thomas, J., Palmer, J., and Shimozaki, S. 2000. A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Percept. Psychophys. 62, 3, 425--451.Google ScholarCross Ref
- Egeth, H. E. and Yantis, S. 1997. Visual attention: Control, representation, and time course. Ann. Rev. Psych. 48, 269--297.Google ScholarCross Ref
- Einhäuser, W., Spain, M., and Perona, P. 2008. Objects predict fixations better than early saliency. J. Vision 8, 14, 1--26.Google ScholarCross Ref
- Elazary, L. and Itti, L. 2008. Interesting objects are visually salient. J. Vision 8, 3:3, 1--15.Google ScholarCross Ref
- Eriksen, C. W. and St. James, J. D. 1986. Visual attention within and around the field of focal attention: A zoom lens model. Percept. Psychophys. 40, 225--240.Google ScholarCross Ref
- Findlay, J. M. and Gilchrist, I. D. 2001. Active vision perspective. In Vision & Attention, M. Jenkin and L. R. Harris, Eds. Springer Verlag, Berlin, 83--103.Google Scholar
- Findlay, J. M. and Walker, R. 1999. A model of saccade generation based on parallel processing and competitive inhibition. Behav. Brain Sci. 22, 661--721.Google ScholarCross Ref
- Fink, G., Dolan, R., Halligan, P., Marshall, J., and Frith, C. 1997. Space-based and object-based visual attention: Shared and specific neural domains. Brain 120, 11, 2013--2028.Google ScholarCross Ref
- Fleming, K. A., Peters II, R. A., and Bodenheimer, R. E. 2006. Image mapping and visual attention on a sensory ego-sphere. In Proceedings of the Conference on Intelligent Robots and Systems (IROS). IEEE, Los Alamitos, CA, 241--246.Google Scholar
- Fragopanagos, N. and Taylor, J. 2006. Modelling the interaction of attention and emotion. Neurocomputing 69, 16--18, 1977--1983.Google ScholarCross Ref
- Fraundorfer, F. and Bischof, H. 2003. Utilizing saliency operators for image matching. In Proceedings of the International Workshop on Attention and Performance in Computer Vision. Springer, Berlin, 17--24.Google Scholar
- Frey, H.-P., Honey, C., and König, P. 2008. What's color got to do with it? The influence of color on visual attention in different categories. J. Vision 8, 14, 1--17.Google ScholarCross Ref
- Frintrop, S. 2005. VOCUS: A visual attention system for object detection and goal-directed search. Ph.D. thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany. Lecture Notes in Artificial Intelligence, vol. 3899, Springer Verlag. Google ScholarDigital Library
- Frintrop, S. 2008. The high repeatability of salient regions. In Proceedings of Workshop on Efficient Strategies for Cognitive Agents in Complex Environments. Springer, Berlin.Google Scholar
- Frintrop, S., Backer, G., and Rome, E. 2005. Goal-directed search with a top-down modulated computational attention system. In Proceedings of the Annual Meeting of the German Association for Pattern Recognition. Springer, Berlin. Google ScholarDigital Library
- Frintrop, S. and Cremers, A. B. 2007. Top-down attention supports visual loop closing. In Proceedings of the European Conference on Mobile Robotics. Springer, Berlin.Google Scholar
- Frintrop, S. and Jensfelt, P. 2008. Attentional landmarks and active gaze control for visual SLAM. IEEE Trans. Rob. 24, 5. Google ScholarDigital Library
- Frintrop, S. and Kessel, M. 2009. Most salient region tracking. In Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, Los Alamitos, CA. Google ScholarDigital Library
- Frintrop, S., Klodt, M., and Rome, E. 2007. A real-time visual attention system using integral images. In Proceedings of the 5th International Conference on Computer Vision Systems.Google Scholar
- Frintrop, S., Königs, A., Hoeller, F., and Schulz, D. 2010. A component-based approach to visual person tracking from a mobile platform. Int. J. So. Rob.Google Scholar
- Frintrop, S., Nüchter, A., Surmann, H., and Hertzberg, J. 2004. Saliency-based object recognition in 3D data. In Proceedings of the International Conference on Intelligent Robots and Systems. IEEE, Los Alamitos, CA, 2167--2172.Google Scholar
- Frintrop, S., Rome, E., Nüchter, A., and Surmann, H. 2005. A bimodal laser-based attention system. J. Comput. Vision Image Understand. 100, 1--2, 124--151. Google ScholarDigital Library
- Fritz, G., Seifert, C., and Paletta, L. 2004. Attentive object detection using an information theoretic saliency measure. In Proceedings of the 2nd International Workshop on Attention and Performance in Computational Vision. Springer, Berlin, 136--143. Google ScholarDigital Library
- Fritz, J. B., Elhilali, M., David, S. V., and Shamma, S. A. 2007. Auditory attention focusing the searchlight on sound. Curr. Opin. Neurobiol. 17, 437--455.Google ScholarCross Ref
- Garey, M. and Johnson, D. S. 1979. Computers and Intractability, A Guide to the Theory of NP-Completeness. Freeman, San Francisco. Google ScholarDigital Library
- Gegenfurtner, K. R. 2003. Cortical mechanisms of color vision. Nat. Rev. Neurosci. 4, 563--572.Google ScholarCross Ref
- Ghazanfar, A. and Schroeder, C. 2006. Is neocortex essentially multisensory? Trends Cogn. Sci. 10, 278--285.Google Scholar
- Giesbrecht, B., Wodorff, M., Song, A., and Mangun, G. 2003. Neural mechanisms of topdown control during spatial and feature attention. Neuroimage 19, 496--512.Google ScholarCross Ref
- Gottlieb, J. P., Kusunoki, M., and Goldberg, M. E. 1998. The representation of visual salience in monkey parietal cortex. Nature 391, 481--484.Google ScholarCross Ref
- Green, D. M. and Swets, J. A. 1966. Signal Detection Theory and Psychophysics. Wiley, New York.Google Scholar
- Hamker, F. H. 2005. The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision. J. Comput. Vision Image Understanding 100, 1--2, 64--106. Google ScholarDigital Library
- Hamker, F. H. 2006. Modeling feature-based attention as an active top-down inference process. BioSystems 86, 91--99.Google ScholarCross Ref
- Harel, J., Koch, C., and Perona, P. 2007. Graph-based visual saliency. In Advances in Neural Information Processing Systems 19, B. Schölkopf, J. Platt, and T. Hoffman, Eds. MIT Press, Cambridge, MA, 545--552.Google Scholar
- Heidemann, G., Rae, R., Bekel, H., Bax, I., and Ritter, H. 2004. Integrating context-free and context-dependent attentional mechanisms for gestural object reference. Mach. Vision Appl. 16, 1, 64--73. Google ScholarDigital Library
- Heinke, D. and Humphreys, G. W. 2003. Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). Psych. Rev. 110, 1, 29--87.Google ScholarCross Ref
- Heinke, D. and Humphreys, G. W. 2004. Computational models of visual selective attention. A review. In Connectionist Models in Psychology, G. Houghton, Ed. Psychology Press, Florence, KY, 273--312.Google Scholar
- Henderson, J. M., Brockmole, J. R., Castelhano, M. S., and Mack, M. 2007. Visual saliency does not account for eye movements during visual search in real-world scenes. In Eye Movements: A Window on Mind and Brain, R. van Gompel, M. Fischer, W. Murray, and R. Hill, Eds. Elsevier, Oxford, 537--562.Google Scholar
- Horowitz, T. S. and Wolfe, J. M. 2003. Memory for rejected distractors in visual search? Visual Cognition 10, 3, 257--298.Google Scholar
- Humphreys, G. W. and Müller, H. J. 1993. Search via recursive rejection (SERR): A connectionist model of visual search. Cognitive Psych. 25, 43--110.Google ScholarCross Ref
- Itti, L. 2002. Real-time high-performance attention focusing in outdoors color video streams. In Proceedings of the SPIE Conference Human Vision and Electronic Imaging. IEEE, Los Alamitos, CA.Google ScholarCross Ref
- Itti, L. 2004. Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Process. 13, 10. Google ScholarDigital Library
- Itti, L. 2005. Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. Visual Cognition 12, 6, 1093--1123.Google ScholarCross Ref
- Itti, L. and Baldi, P. 2009. Bayesian surprise attracts human attention. Vision Res. 49, 10, 1295--1306.Google ScholarCross Ref
- Itti, L., Dhavale, N., and Pighin, F. 2003. Realistic avatar eye and head animation using a neurobiological model of visual attention. In Proceedings of the SPIE 48th Annual International Symposium on Optical Science and Technology. IEEE, Los Alamitos, CA.Google Scholar
- Itti, L. and Koch, C. 2001a. Computational modeling of visual attention. Nat. Rev. Neurosci. 2, 3, 194--203.Google ScholarCross Ref
- Itti, L. and Koch, C. 2001b. Feature combination strategies for saliency-based visual attention systems. J. Electr. Imaging 10, 1, 161--169.Google ScholarCross Ref
- Itti, L., Koch, C., and Niebur, E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 11, 1254--1259. Google ScholarDigital Library
- Johansson, R., Westling, G., Backstrom, A., and Flanagan, J. 2001. Eye-hand coordination in object manipulation. J. Neurosci. 21, 17, 6917--6932.Google ScholarCross Ref
- Johnson, A. and Proctor, R. 2003. Attention: Theory and Practice. Sage Publications, Newbury Park, CA.Google Scholar
- Jonides, J. 1981. Voluntary versus automatic control over the mind's eye movements. In Attention and Performance IX, A. D. Long, Ed. Lawrence Erlbaum Associates, NJ, 187--203.Google Scholar
- Kadir, T. and Brady, M. 2001. Saliency, scale and image description. Int. J. Comput. Vision 45, 2, 83--105. Google ScholarDigital Library
- Kahneman, D. and Treisman, A. 1992. The reviewing of object files: Object-specific integration of information. Cognitive Psych. 24, 175--219.Google ScholarCross Ref
- Kandel, E. R., Schwartz, J. H., and Jessell, T. M. 1996. Essentials of Neural Science and Behavior. McGraw-Hill/Appleton & Lange, New York.Google Scholar
- Kastner, S. and Ungerleider, L. G. 2001. The neural basis of biased competition in human visual cortex. Neuropsychologia 39, 1263--1276.Google ScholarCross Ref
- Koch, C. and Ullman, S. 1985. Shifts in selective visual attention: Towards the underlying neural circuitry. Hum. Neurobiol. 4, 4, 219--227.Google Scholar
- Kootstra, G., Nederveen, A., and de Boer, B. 2008. Paying attention to symmetry. In Proceedings of the British Machine Vision Conference. Springer, Berlin.Google ScholarCross Ref
- Kunar, M., Flusberg, S., and Wolfe, J. 2008. The role of memory and restricted context in repeated visual search. Percept. Psychophys. 70, 314--328.Google ScholarCross Ref
- Land, M. F. 2006. Eye-movements and the control of actions in everyday life. Prog. Retinal Eye Res. 25, 296--324.Google ScholarCross Ref
- Lee, E. A. 2008. Cyber physical systems: Design challenges. Tech. rep. UCB/EECS-2008-8, EECS Department, University of California, Berkeley.Google Scholar
- Lee, K., Buxton, H., and Feng, J. 2003. Selective attention for cue-guided search using a spiking neural network. In Proceedings of the International Workshop on Attention and Performance in Computer Vision. IEEE, Los Alamitos, CA, 55--62.Google Scholar
- Levin, D. 1996. Classifying faces by race: the structure of face categories. J. Exp. Psych. 22, 1364--1382.Google Scholar
- Li, Z. 2005. The primary visual cortex creates a bottom-up saliency map. In Neurobiology of Attention, L. Itti, G. Rees, and J. Tsotsos, Eds. Elsevier Academic Press.Google Scholar
- Liu, T., Slotnick, S. D., Serences, J. T., and Yantis, S. 2003. Cortical mechanisms of feature-based intentional control. Cerebral Cortex 13, 12.Google ScholarCross Ref
- Livingstone, M. S. and Hubel, D. H. 1987. Psychophysical evidence for separate channels for the perception of form, color, movement, and depth. J. Neurosci. 7, 11, 3416--3468.Google ScholarCross Ref
- Logan, G. D. 1996. The CODE theory of visual attention: an integration of space-based and object-based attention. Psych. Rev. 103, 603--649.Google ScholarCross Ref
- Lowe, D. G. 2004. Distinctive image features from scale-invariant key points. Int. J. Comput. Vision 60, 2, 91--110. Google ScholarDigital Library
- Maki, A., Nordlund, P., and Eklundh, J.-O. 2000. Attentional scene segmentation: Integrating depth and motion. Comput. Vision Image Understanding 78, 3, 351--373. Google ScholarDigital Library
- Marr, D. 1982. VISION -- A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Company, New York. Google ScholarDigital Library
- Maunsell, J. H. R. 1995. The brain's visual world: Representation of visual targets in cerebral cortex. Science 270, 764--769.Google ScholarCross Ref
- May, S., Klodt, M., and Rome, E. 2007. GPU-accelerated Affordance Cueing based on Visual Attention. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS). IEEE, Los Alamitos, CA, 3385--3390.Google Scholar
- Mazer, J. A. and Gallant, J. L. 2003. Goal-related activity in V4 during free viewing visual search. Evidence for a ventral stream visual salience map. Neuron 40, 6, 1241--50.Google ScholarCross Ref
- McMains, S. A. and Somers, D. C. 2004. Multiple spotlights of attentional selection in human visual cortex. Neuron 42, 677--686.Google ScholarCross Ref
- Mertsching, B., Bollmann, M., Hoischen, R., and Schmalz, S. 1999. The neural active vision system. In Handbook of Computer Vision and Applications, B. Jähne, H. Haussecke, and P. Geissler, Eds. vol. 3. Academic Press, 543--568.Google Scholar
- Miau, F., Papageorgiou, C., and Itti, L. 2001. Neuromorphic algorithms for computer vision and attention. In Proceedings of the 46th Annual SPIE International Symposium on Optical Science and Technology. IEEE, Los Alamitos, CA, 12--23.Google Scholar
- Milanese, R. 1993. Detecting salient regions in an image: From biological evidence to computer implementation. Ph.D. thesis, University of Geneva, Switzerland.Google Scholar
- Milanese, R., Wechsler, H., Gil, S., Bost, J., and Pun, T. 1994. Integration of bottom-up and top-down cues for visual attention using non-linear relaxation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA, 781--785.Google Scholar
- Mitri, S., Frintrop, S., Pervölz, K., Surmann, H., and Nüchter, A. 2005. Robust object detection at regions of interest with an application in ball recognition. In Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, Los Alamitos, CA, 126--131.Google Scholar
- Mozer, M. C. 1987. Early parallel processing in reading: A connectionist approach. In Attention and Performance XII: The Psychology of Reading, M. Coltheart, Ed. Lawrence Erlbaum Associated Ltd., Philadelphia, 83--104.Google Scholar
- Muhl, C., Nagai, Y., and Sagerer, G. 2007. On constructing a communicative space in HRI. In Proceedings of the 30th German Conference on Artificial Intelligence, J. Hertzberg, M. Beetz, and R. Englert, Eds. Springer, Berlin. Google ScholarDigital Library
- Nagai, Y. 2009. From bottom-up visual attention to robot action learning. In Proceedings of the 8th International IEEE Conference on Development and Learning. IEEE, Los Alamitos, CA. Google ScholarDigital Library
- Nakayama, K. and Mackeben, M. 1989. Sustained and transient components of focal visual attention. Vision Res. 29, 1631--1647.Google ScholarCross Ref
- Nakayama, K. and Silverman, G. H. 1986. Serial and parallel processing of visual feature conjunctions. Nature 320, 264--265.Google ScholarCross Ref
- Navalpakkam, V. and Itti, L. 2006a. An integrated model of top-down and bottom-up attention for optimizing detection speed. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA. Google ScholarDigital Library
- Navalpakkam, V. and Itti, L. 2006b. Top-down attention selection is fine-grained. J. Vision 6, 11, 1180--1193.Google ScholarCross Ref
- Navalpakkam, V., Rebesco, J., and Itti, L. 2004. Modeling the influence of knowledge of the target and distractors on visual search. J. Vision 4, 8, 690.Google ScholarCross Ref
- Navalpakkam, V., Rebesco, J., and Itti, L. 2005. Modeling the influence of task on attention. Vision Res. 45, 2, 205--231.Google ScholarCross Ref
- Neisser, U. 1967. Cognitive Psychology. Appleton-Century-Crofts, New York.Google Scholar
- Nickerson, S. B., Jasiobedzki, P., Wilkes, D., Jenkin, M., Milios, E., Tsotsos, J. K., Jepson, A., and Bains, O. N. 1998. The ARK project: Autonomous mobile robots for known industrial environments. Rob. Auton. Syst. 25, 1--2, 83--104.Google ScholarCross Ref
- Nothdurft, H.-C. 2005. Salience of feature contrast. In Neurobiology of Attention, L. Itti, G. Rees, and J. K. Tsotsos, Eds. Elsevier, Burlington, MA, 233--239.Google Scholar
- Ogawa, T. and Komatsu, H. 2004. Target selection in area V4 during a multidimensional visual search task. J. Neurosci. 24, 28, 6371--6382.Google ScholarCross Ref
- Oliva, A. 2005. Gist of the scene. In Neurobiology of Attention, L. Itti, G. Rees, and J. Tsotsos, Eds. Elsevier Academic Press, 251--257.Google Scholar
- Oliva, A., Torralba, A., Castelhano, M. S., and Henderson, J. M. 2003. Top-down control of visual attention in object detection. In Proceedings of the International Conference on Image Processing. IEEE, Los Alamitos, CA, 253--256.Google Scholar
- Olshausen, B., Anderson, C., and van Essen, D. 1993. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J. Neurosci. 13, 11, 4700--4719.Google ScholarCross Ref
- Olshausen, B. A. and Field, D. J. 2005. How close are we to understanding V1? Neural Comput. 17, 8, 1665--1699. Google ScholarDigital Library
- Olshausen, B. A. and Field, D. J. 2006. What is the other 85% of V1 doing? In 23 Problems in Systems Neuroscience, L. V. Hemmen and T. Sejnowslti, Eds. Oxford University Press, Oxford, UK.Google Scholar
- Ouerhani, N. 2003. Visual attention: From bio-inspired modeling to real-time implementation. Ph.D. thesis, Institut de Microtechnique Université de Neuchâtel, Switzerland.Google Scholar
- Ouerhani, N., Bur, A., and Hügli, H. 2005. Visual attention-based robot self-localization. In Proceedings of the European Conference on Mobile Robotics (ECMR 2005). IEEE, Los Alamitos, CA, 8--13.Google Scholar
- Ouerhani, N. and Hügli, H. 2000. Computing visual attention from scene depth. In Proceedings of the International Conference on Pattern Recognition (ICPR 2000). Vol. 1. IEEE, Los Alamitos, CA, 375--378. Google ScholarDigital Library
- Ouerhani, N., Jost, T., Bur, A., and Hügli, H. 2006. Cue normalization schemes in saliency-based visual attention models. In Proceedings International Cognitive Vision Workshop. Springer, Berlin.Google Scholar
- Ouerhani, N., von Wartburg, R., Hügli, H., and Müri, R. 2004. Empirical validation of the saliency-based model of visual attention. Electr. Lett. Comput. Vision Image Anal. 3, 1, 13--24.Google ScholarCross Ref
- Palmer, J., Ames, C., and Lindsey, D. 1993. Measuring the effect of attention on simple visual search. J. of Experimental Psychology. Hum. Percept. Perform. 19, 1, 108--130.Google ScholarCross Ref
- Palmer, S. E. 1999. Vision Science, Photons to Phenomenology. MIT Press, Cambridge, MA.Google Scholar
- Parkhurst, D., Law, K., and Niebur, E. 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Res. 42, 1, 107--123.Google ScholarCross Ref
- Pashler, H. 1997. The Psychology of Attention. MIT Press, Cambridge, MA.Google Scholar
- Pessoa, L. and Exel, S. 1999. Attentional strategies for object recognition. In Proceedings of the International Work Conference on Artificial and Natural Neural Networks. Springer, Berlin, 850--859. Google ScholarDigital Library
- Peters, R., Iyer, A., Itti, L., and Koch, C. 2005. Components of bottom-up gaze allocation in natural images. Vision Res. 45, 2397--2416.Google ScholarCross Ref
- Peters, R. J. and Itti, L. 2008. Applying computational tools to predict gaze direction in interactive visual environments. ACM Trans. Appl. Percept. 5, 2. Google ScholarDigital Library
- Phaf, R. H., van der Heijden, A. H. C., and Hudson, P. T. W. 1990. SLAM: A connectionist model for attention in visual selection tasks. Cognitive Psych. 22, 273--341.Google ScholarCross Ref
- Posner, M. and Cohen, Y. 1984. Components of visual orienting. In Attention and Performance X, H. Bouma and D. Bouwhuis, Eds. Erlbaum, London, 531--556.Google Scholar
- Posner, M. I. 1980. Orienting of attention. Q. J. Exp. Psych. 32, 3--25.Google ScholarCross Ref
- Posner, M. I. and Petersen, S. E. 1990. The attentional system of the human brain. Ann. Rev. Neurosci. 13, 25--42.Google ScholarCross Ref
- Postma, E. 1994. Scan: A neural model of covert attention. Ph.D. thesis, Rijksuniversiteit Limburg, Wageningen.Google Scholar
- Pylyshyn, Z. and Storm, R. 1988. Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision 3, 179--197.Google ScholarCross Ref
- Pylyshyn, Z. W. 2003. Seeing and Visualizing: It's Not What You Think. MIT Press, Cambridge, MA.Google Scholar
- Rae, R. 2000. Gestikbasierte Mensch-Maschine-Kommunikation auf der Grundlage visueller Aufmerksamkeit und Adaptivität. Ph.D. thesis, Universität Bielefeld, Germany.Google Scholar
- Ramström, O. and Christensen, H. I. 2002. Visual attention using game theory. In Proceedings of the Workshop on Biologically Motivated Computer Vision. Springer, Berlin. Google ScholarDigital Library
- Ramström, O. and Christensen, H. I. 2004. Object-based visual attention: Searching for objects defined by size. In Proceedings of International Workshop on Attention and Performance in Computational Vision. Springer, Berlin, 9--16.Google Scholar
- Rao, R., Zelinsky, G., Hayhoe, M., and Ballard, D. 2002. Eye-movements in iconic visual search. Vision Res. 42, 1447--1463.Google ScholarCross Ref
- Rasolzadeh, B., Björkman, M., Huebner, K., and Kragic, D. 2009. An active vision system for detecting, fixating and manipulating objects in real world. Int. J. Rob. Res. Google ScholarDigital Library
- Rauschenberger, R. 2003. Attentional capture by auto- and allo-cues. Psychonomic Bull. Rev. 10, 4, 814--842.Google ScholarCross Ref
- Rensink, R. A. 2000. The dynamic representation of scenes. Visual Cognition 7, 17--42.Google ScholarCross Ref
- Rensink, R. A., O'Regan, J. K., and Clark, J. J. 1997. To see or not to see: The need for attention to perceive changes in scenes. Psych. Sci. 8, 368--373.Google ScholarCross Ref
- Riesenhuber, M. and Poggio, T. 1999. Hierarchical models of object recognition in cortex. Nature Neurosci. 2, 11, 1019--1025.Google ScholarCross Ref
- Rosenholtz, R. 2001. Search asymmetries? What search asymmetries? Percept. Psychophys. 63, 3, 476--489.Google ScholarCross Ref
- Rotenstein, A., Andreopoulos, A., Fazl, E., Jacob, D., Robinson, M., Shubina, K., Zhu, Y., and Tsotsos, J. 2007. Towards the dream of intelligent, visually-guided wheelchairs. In Proceedings of the 2nd International Conference on Technology and Aging.Google Scholar
- Rothenstein, A. and Tsotsos, J. 2006a. Attention links sensing to recognition. Image Vision Comput. J. 26, 1, 114--126. Google ScholarDigital Library
- Rothenstein, A. and Tsotsos, J. 2006b. Selective tuning: Feature binding through selective attention. In Proceedings of International Conference on Artificial Neural Networks. IEEE, Los Alamitos, CA. Google ScholarDigital Library
- Rybak, I., Gusakova, V., Golovan, A., Podladchikova, L., and Shevtsova, N. 1998. A model of attention-guided visual perception and recognition. Vision Res. 38, 2387--2400.Google ScholarCross Ref
- Sabra, A. I. 1989. The Optics of Ibn Al-Haytham. The Warburg Institute, University of London.Google Scholar
- Salah, A., Alpaydin, E., and Akrun, L. 2002. A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24, 3, 420--425. Google ScholarDigital Library
- Sandini, G. and Metta, G. 2002. Retina-like sensors: Motivations, technology and applications. In Sensors and Sensing in Biology and Engineering. Springer Verlag, Berlin.Google Scholar
- Schauerte, B., Richarz, J., Plötz, T., Thurau, C., and Fink, G. A. 2009. Multi-modal and multi-camera attention in smart environments. In Proceedings of Multi-Modal Interfaces and Workshop on Machine Learning for Multi-Modal Interaction. ACM, New York. Google ScholarDigital Library
- Scheier, C. and Egner, S. 1997. Visual attention in a mobile robot. In Proceedings of the IEEE International Symposium on Industrial Electronics. IEEE, Los Alamitos, CA, 48--53.Google Scholar
- Scholl, B. J. 2001. Objects and attention: The state of the art. Cognition 80, 1--46.Google ScholarCross Ref
- Shulman, G., Remington, R., and McLean, J. 1979. Moving attention through visual space. J. Exp. Psych. 5, 3, 522--526.Google Scholar
- Siagian, C. and Itti, L. 2009. Biologically inspired mobile robot vision localization. IEEE Trans. Rob. 25, 4, 861--873. Google ScholarDigital Library
- Simons, D. J. and Levin, D. T. 1997. Change blindness. Trends Cognitive Sci. 1, 261--267.Google ScholarCross Ref
- Styles, E. A. 1997. The Psychology of Attention. Psychology Press Ltd, Florence, KY.Google Scholar
- Sumner, P. and Mollon, J. 2000. Catarrhine photopigments are optimized for detecting targets against a foliage background. J. Exp. Biol. 203, 1963--1986.Google Scholar
- Sun, Y. and Fisher, R. 2003. Object-based visual attention for computer vision. Artif. Intell. 146, 1, 77--123. Google ScholarDigital Library
- Tatler, B. W. 2007. The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. J. Vision 14, 7, 1--17.Google Scholar
- Tatler, B. W., Baddeley, R. J., and Gilchrist, I. D. 2005. Visual correlates of fixation selection: effects of scale and time. Vision Res. 45, 643--659.Google ScholarCross Ref
- Tatler, B. W., Baddeley, R. J., and Vincent, B. T. 2006. The long and the short of it: Spatial statistics at fixation vary with saccade amplitude and task. Vision Res. 46, 1857--1862.Google ScholarCross Ref
- Theeuwes, J. 2004. Top-down search strategies cannot override attentional capture. Psychonomic Bull. Rev. 11, 65--70.Google ScholarCross Ref
- Torralba, A. 2003a. Contextual priming for object detection. Int. J. Comput.Vision 53, 2, 169--191. Google ScholarDigital Library
- Torralba, A. 2003b. Modeling global scene factors in attention. J. Opt. Soc. Am. 20, 7, 1407--1418.Google ScholarCross Ref
- Treisman, A. M. 1993. The perception of features and objects. In Attention: Selection, Awareness, and Control, A. Baddeley and L. Weiskrantz, Eds. Clarendon Press, Oxford, 5--35.Google Scholar
- Treisman, A. M. and Gelade, G. 1980. A feature integration theory of attention. Cognitive Psych. 12, 97--136.Google ScholarCross Ref
- Treisman, A. M. and Gormican, S. 1988. Feature analysis in early vision: Evidence from search asymmetries. Psych. Rev. 95, 1, 15--48.Google ScholarCross Ref
- Tsotsos, J., Rodriguez-Sanchez, A., Rothenstein, A., and Simine, E. 2008. Different binding strategies for the different stages of visual recognition. Brain Res. 1225, 119--132.Google ScholarCross Ref
- Tsotsos, J. K. 1987. A “complexity level” analysis of vision. In Proceedings of the International Conference on Computer Vision: Human and Machine Vision Workshop. IEEE, Los Alamitos, CA.Google Scholar
- Tsotsos, J. K. 1990. Analyzing vision at the complexity level. Behav. Brain Sci. 13, 3, 423--445.Google ScholarCross Ref
- Tsotsos, J. K. 1993. An inhibitory beam for attentional selection. In Spatial Vision in Humans and Robots, L. R. Harris and M. Jenkin, Eds. Cambridge University Press, Cambridge, UK, 313--331. Google ScholarDigital Library
- Tsotsos, J. K., Culhane, S. M., Wai, W. Y. K., Lai, Y., Davis, N., and Nuflo, F. 1995. Modeling visual attention via selective tuning. Artif. Intell. 78, 1-2, 507--545. Google ScholarDigital Library
- Tsotsos, J. K., Liu, Y., Martinez-Trujillo, J. C., Pomplun, M., Simine, E., and Zhou, K. 2005. Attenting to visual motion. J. Comput. Vision Image Understanding 100, 1-2, 3--40. Google ScholarDigital Library
- Tsotsos, J. K., Verghese, G., Stevenson, S., Black, M., Metaxas, D., Culhane, S., Dickinson, S., Jenkin, M., Jepson, A., et al. 1998. PLAYBOT: A visually-guided robot to assist physically disabled children in play. Image Vision Comput. 16, 275--292.Google ScholarCross Ref
- Tuytelaars, T. and Mikolajczyk, K. 2007. Local invariant feature detectors: A survey. Found. Trends Comput. Graphics Vision 3, 3, 177--280. Google ScholarDigital Library
- van Oeffelen, M. P. and Vos, P. G. 1982. Configurational effects on the enumeration of dots: Counting by groups. Memory Cognition 10, 396--404.Google ScholarCross Ref
- Vecera, S. and Farah, M. 1994. Does visual attention select objects or locations? J. Exp. Psych. 123, 2, 146--160.Google ScholarCross Ref
- Verghese, P. 2001. Visual search and attention: A signal detection theory approach. Neuron 31, 523--535.Google ScholarCross Ref
- Vickery, T. J., King, L.-W., and Jiang, Y. 2005. Setting up the target template in visual search. J. Vision 5, 1, 81--92.Google ScholarCross Ref
- Vijayakumar, S., Conradt, J., Shibata, T., and Schaal, S. 2001. Overt visual attention for a humanoid robot. In Proceedings of the International Conference on Intelligence in Robotics and Autonomous Systems (IROS 2001). ACM, New York, 2332--2337.Google Scholar
- Vincent, B. T., Troscianko, T., and Gilchrist, I. D. 2007. Investigating a space-variant weighted salience account of visual selection. Vision Res. 47, 1809--1820.Google ScholarCross Ref
- Viola, P. and Jones, M. J. 2004. Robust real-time face detection. Int. J. Comput. Vision 57, 2, 137--154. Google ScholarDigital Library
- von Helmholtz, H. 1896. Handbuch der physiologischen Optik. Von Leopold Voss Verlag, Hamburg, Germany. (an English Quote is included in Nakayama & Mackeben, 1989).Google Scholar
- Walther, D. 2006. Interactions of visual attention and object recognition: computational modeling, algorithms, and psychophysics. Ph.D. thesis, California Institute of Technology, Pasadena, CA.Google Scholar
- Walther, D., Edgington, D. R., and Koch, C. 2004. Detection and tracking of objects in underwater video. In Proceedings of the International Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA.Google Scholar
- Walther, D. and Koch, C. 2007. Attention in hierarchical models of object recognition. Comput. Neurosci. 165, 57--78.Google Scholar
- Wells, A. and Matthews, G. 1994. Attention and Emotion: A Clinical Perspective. Psychology Press, Florence, KY.Google Scholar
- Wolfe, J. M. 1994. Guided search 2.0: A revised model of visual search. Psychonomic Bull. Rev. 1, 2, 202--238.Google ScholarCross Ref
- Wolfe, J. M. 1998a. Visual search. In Attention, H. Pashler, Ed. Psychology Press, Florence, KY, 13--74.Google Scholar
- Wolfe, J. M. 1998b. What can 1,000,000 trials tell us about visual search? Psych. Sci. 9, 1, 33--39.Google ScholarCross Ref
- Wolfe, J. M. 2001a. Asymmetries in visual search: An introduction. Percept. Psychophys. 63, 3, 381--389.Google ScholarCross Ref
- Wolfe, J. M. 2001b. Guided search 4.0: A guided search model that does not require memory for rejected distractors. J. Vision 1, 3, 349a.Google ScholarCross Ref
- Wolfe, J. M. 2007. Guided search 4.0: Current progress with a model of visual search. In Integrated Models of Cognitive Systems, W. D. Gray, Ed. Oxford University Press, Oxford, UK.Google Scholar
- Wolfe, J. M., Cave, K., and Franzel, S. 1989. Guided search: An alternative to the feature integration model for visual search. J. Exp. Psych. 15, 419--433.Google Scholar
- Wolfe, J. M. and Gancarz, G. 1996. Guided Search 3.0: Basic and Clinical Applications of Vision Science. Kluwer Academic, The Netherlands, 189--192.Google Scholar
- Wolfe, J. M., Horowitz, T., Kenner, N., Hyle, M., and Vasan, N. 2004. How fast can you change your mind? The speed of top-down guidance in visual search. Vision Res. 44, 1411--1426.Google ScholarCross Ref
- Wolfe, J. M. and Horowitz, T. S. 2004. What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5, 1--7.Google ScholarCross Ref
- Xu, T., Chenkov, N., Kühnlenz, K., and Buss, M. 2009. Autonomous switching of top-down and bottom-up attention selection for vision guided mobile robots. In Proceedings of the International Conference on Intelligent Robots and Systems. ACM, New York. Google ScholarDigital Library
- Xu, T., Pototschnig, T., Kühnlenz, K., and Buss, M. 2009. A high-speed multi-GPU implementation of bottom-up attention using CUDA. In Proceedings of the International Conference on Robotics and Automation. IEEE, Los Alamitos, CA. Google ScholarDigital Library
- Yantis, S. 2000. Goal-directed and stimulus-driven determinants of attentional control. In Attention and Performance, S. Monsell and J. Driver, Eds. Vol. 18. MIT Press, Cambridge, MA.Google Scholar
- Yantis, S., Ach, J. S., Serences, J., Carlson, R., Steinmetz, M., Pekar, J., and Courtney, S. 2002. Transient neural activity in human parietal cortex during spatial attention shifts. Nat. Neurosci. 5, 995--1002.Google ScholarCross Ref
- Yantis, S. and Serences, J. T. 2003. Cortical mechanisms of space-based and object-based attentional control. Curr. Opin. Neurobiol. 13, 187--193.Google ScholarCross Ref
- Yarbus, A. L. 1967. Eye-Movements and Vision. Plenum Press, New York.Google Scholar
- Zeki, S. 1993. A Vision of the Brain. Blackwell Scientific, Cambridge, MA.Google Scholar
- Zelinsky, G. J. and Sheinberg, D. L. 1997. Eye-movements during parallel-serial visual search. J. Exp. Psych. Hum. Percept. Perform. 23, 1, 244--262.Google ScholarCross Ref
Index Terms
- Computational visual attention systems and their cognitive foundations: A survey
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